Instructions to use cemanaral/brand-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use cemanaral/brand-detection with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://cemanaral/brand-detection") - Notebooks
- Google Colab
- Kaggle
Set `library_name` to `tf-keras`.
#1
by Wauplin HF Staff - opened
README.md
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
---
|
| 2 |
-
library_name: keras
|
| 3 |
tags:
|
| 4 |
-
|
| 5 |
widget:
|
| 6 |
- src: https://datasets-server.huggingface.co/assets/AIARTCHAN/lora-Hyundai_Equus_1999/--/AIARTCHAN--lora-Hyundai_Equus_1999/train/0/image/image.jpg
|
| 7 |
example_title: Hyundai
|
|
|
|
| 1 |
---
|
| 2 |
+
library_name: tf-keras
|
| 3 |
tags:
|
| 4 |
+
- image-classification
|
| 5 |
widget:
|
| 6 |
- src: https://datasets-server.huggingface.co/assets/AIARTCHAN/lora-Hyundai_Equus_1999/--/AIARTCHAN--lora-Hyundai_Equus_1999/train/0/image/image.jpg
|
| 7 |
example_title: Hyundai
|